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Memristive devices with a simple structure are not only very small but also very versatile, which makes them an ideal candidate used for the next generation computing system in the post-Si era. The working mechanism of the devices and a family of nanodevices built based on this working mechanism are introduced first followed by some proposed applications of(More)
We present an atomic-scale teleoperation system that uses a head-mounted display and force-feedback manipulator arm for a user interface and a Scanning Tunneling Microscope (STM) as a sensor and effector. The system approximates presence at the atomic scale, placing the scientist on the surface, in control, w h i l e the experiment is happening. A scientist(More)
— This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results(More)
— A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach,(More)
Vector-matrix multiplication dominates the computation time and energy for many workloads, particularly neural network algorithms and linear transforms (e.g, the Discrete Fourier Transform). Utilizing the natural current accumulation feature of memristor crossbar, we developed the Dot-Product Engine (DPE) as a high density, high power efficiency accelerator(More)